We therefore want to compare our JPFG model with the factor graph model with only consider the personal connections or skill connections, and analysis the contribution of each kinds of connection.

maxEnt-Personal employs the personal connections as additional features incorporated with textual features to build the maximum entropy classification.


Evaluation on a largescale dataset shows that our joint model performs much better than several baselines.

it shows that the performance on personal skill inference can be greatly improved by incorporating skill connection information.


Given these equivalence classes, we define the matrix grammar The rules R m of G m are then designed to enforce the above mentioned tree well formedness constraints.

some possible such rules are given in Table 1 using ID/LP notation (Gazdar et al., 1985).an immediate dominance rule of the form a → b , c is expanded as two rules a → b c and a → c b.


On the other hand it makes inference incomplete (the parser may fail to find a solution even if it exists) and does not guarantee the solution to be optimal.

equation 1 is replaced by an approximation: The weight estimation procedure is performed by the averaged percetron algorithm (Collins, 2002).


We consider the linguistics perspective, where it is pragmatically viewed as a regulated sequence of speech or text (Walton and Godden, 2006).

we analyze monological argumentations in written text as opposed to dialogical argumentations where participants persuade each other with arguments (Cabrio and Villata, 2012).


The following function hence can be understood as a shallow version of the rhetorical structure theory (Mann and Thompson, 1988): The argumentation structure of a text is bound to the domain at hand through the text's content.

a review text discusses a subset of the domain concepts C that are associated to a product or service, each being referred to in one or more statements.


We choose the provided sentiment scale from 0 to 2, so we can logically map the scale of the hotel reviews (1-5) to it for a domain transfer.

scores 1-2 are mapped to 0, 3 to 1, and 4-5 to 2.


For the comparison to (Pang and Lee, 2005), we predict the scores of the movie reviews using classification, which additionally stresses the domain change.

we measure the accuracy of a linear 1-vs.-1 multi-class SVM with probability estimates and normalization.


The disparity of the Author d dataset is additionally emphasized by the different proportions of opinions in the evaluated text corpora.

79.7% of all statements in the ArguAna TripAdvisor corpus are opinions, but only 36.5% of the sentences of Author d are classified as subjective.


Based on this shallow model of argumentation, we combine existing sentiment analysis approaches with novel features that capture the abstract overall argumentation structure of reviews irrespective of their domain and their linguistic style.

we learn common sequences of local sentiment in reviews through clustering in order to then compare a given review to each of these learned sentiment flow patterns.


In the GALE corpus, we identified 1,178 (out of total 6,255) source sentences with reference translations containing more than one sentence.

sentence/discourse mismatch between Chinese and English occurs for 18.83% of the data.


We investigate such implicit/explicit mismatch using direct projection.

we study the cases in which an English discourse connective is not aligned to any part of its corresponding Chinese sentence.


(2011), English and French, are closely related European languages; here we investigate such differences in ambiguities between English and Chinese connectives.

using the connectives collected from direct projection, we inspect the relations annotated for these connectives in the Chinese Discourse Treebank development set, and extract connectives such that the majority sense they signal constitutes less than 90% of their total occurrences.


These scores indicate the superiority of SimM over DivS.

separating the mixture of topics of a fragment into multiple topically-separated queries mitigates the negative effect of the mixture of topics on the suggestions.


Although we can expect little variability in the usage of discourse relations in parallel texts, this is not the case for DCs.

translated texts may not always reproduce DCs of the source texts.


This was done to ensure that no ambiguity would exist in the discourse relation of the French sentences, once we transfer the discourse relation from English to French.

we can label each French sentence with a single discourse relation, that of its English translation.


This has been done, since not only the precision of the parser in detecting discourse relation in the absence of DC is very low (24.54%), but also we would not expect implicit relations to help us to identify DCs in French.

a translator only occasionally inserts DCs in a translation and therefore we would not expect that too many DCs would exist in the translation of sentences with an implicit discourse relation.


Word-Alignment Filter: This filter removes any DC candidate that does not align with any part of an English DC.

this filter keeps any consecutive words in the French text if at least one of its composing words aligns to at least one word of an English DC when using a word-alignment model.


The syntactic filters exploit this property and remove any constituent that is not categorized as a DC.

these filters keep only Prepositional Phrases (PP), Coordinate Phrases (CP) or Adverbial Phrases (ADVP).


We have modeled the task of inducing DCs as a binary classification and tried to evaluate it using precision and recall.

by choosing a threshold for LLR, we can  label each potential DC candidate as "DC" if its LLR is above the threshold or "non-DC" otherwise.


The precisions of all the four relations are increased.

the precisions of Temporal, Contingency, and Comparison gain more than 10%.


Moreover, M4 is better than M2 in F-score for all the relations.

the precisions of Temporal, Contingency, and Comparison recognition by M4 are greatly increased.


In particular, the precisions of Temporal, Contingency, and Comparison recognition by M4 are greatly increased.

the boosting algorithm tends to correct those instances that are originally misclassified into the Expansion relation.


When both mentions are detected as a singleton by the singleton detection module, the value of all their corresponding relations will be set to zero.

r(m, n) is set to zero when both n and m have been detected as a singleton.


In this section, we discuss how to derive effective features from both Zhihu and Weibo.

we mainly study how to leverage Weibo knowledge for the current task.


Most existing models work on individual words.

they treat each topic as a distribution over words.


Word to phrase: When an individual word is assigned to a topic (analogous to drawing a ball of a certain color), each phrase containing the word will be promoted, meaning that the phrase will be added to the same topic with a small count.

a fraction of the phrase will be assigned to the topic.


Phrase to word: When a phrase is assigned to a topic, each component word in it is also promoted with a certain small count.

each word is also assigned the topic by a certain amount.


The goal of this section is to represent the linking relation between an opinion word and an opinion target by a n-element vector as we do during word representation.

we combine embedding vectors of words in a linking relation by a recursive autoencoder (Socher et al., 2011) according to syntactic dependency structure.


Once the set of all relevant patterns P = p 1 , p 2 , ..., p n have been computed a DSM V is constructed.

the DSM constitutes a Σ V based on the list of word pairs of interest, and every word pair w i , w j of interest has an associated vector v ij .


The dataset used in this experiment is from the SemEval-2012 task 2 on measuring the degree of relational similarity (Jurgens et al., 2012), since it characterizes a number of very distinct and interesting semantic relations.

it consists of an aggregated set of 3464 word pairs evidencing 10 kinds of semantic relations.


If the count in our lexicon is below the occurrence threshold, we always carry out this split, which means we remove the entry from the lexicon and (after all n-grams of that length have been processed, so as to avoid ordering effects) add its counts to the counts of n-grams of its best decomposition.

if the count is above the threshold, we preserve the full entry (for entries of length 3 or greater) only if the following inequality is true for each subsegment w j,k in the full entry w 1,n : the ratio (expressed as a difference of logarithms) between the count of the segment and the full unsegmented entry (in our preliminary lexicon) is lower than the ratio of the predictability (as defined in our discussion of prediction-based decomposition) of the words in the segment with the context of the full entry to the predictability of words with only the context included within the segment (which is just pred(w j,k )).


If the count is above the threshold, we preserve the full entry (for entries of length 3 or greater) only if the following inequality is true for each subsegment w j,k in the full entry w 1,n : That is, the ratio (expressed as a difference of logarithms) between the count of the segment and the full unsegmented entry (in our preliminary lexicon) is lower than the ratio of the predictability (as defined in our discussion of prediction-based decomposition) of the words in the segment with the context of the full entry to the predictability of words with only the context included within the segment (which is just pred(w j,k )).

we preserve only longer multiword sequences in our lexicon when any decrease in the probability of the full entry relative to its smaller components 2 is fully offset by an increase in the conditional probability of the individual words of that segment when the larger context from the full segment is available.


To further improve the quality of projected structures, we add dependencies with high marginal probabilities according to the target parser.

if a target word w j obtain a head word w i after projection, and if another word w k has higher marginal probability than a supplement threshold λ s to be the head word of w j , then we also add the dependency (k, j) into the projected structure.


Specifically, if a target word w j obtain a head word w i after projection, and if another word w k has higher marginal probability than a supplement threshold λ s to be the head word of w j , then we also add the dependency (k, j) into the projected structure.

we allow one word to have multiple heads so that the projected structure can cover more correct dependencies.


To handle these issues, we first convert the projected structures into parse forests, and then propose a generalized training technique based on ambiguous labelings to make use of the projected instances.

if a word does not have head words after projection, we simply add into the projected structure all possible words as its candidate heads as long as the newly-added dependency does not cross any projected dependencies, as illustrated in Figure 1(c).


The goal of the training procedure is to maximize the likelihood of the training data, and the model is updated to improve the probabilities of parse forests, instead of single parse trees.

the model has the flexibility to distribute the probability mass among the parse trees inside the forest, as long as the probability of the forest improves.


Training phase optimizes the weight parameters in order to maximize the difference in posterior probabilities among the correct noun phrase and the other candidates.

the model of case M c is learnt by minimizing the following loss function c .


First, comparing methods a) Adaptation and b) NAIST training, Adaptation was better than the NAIST training for most types (The ♥ mark denotes 'significantly better').

the total F-measures of all cases were significantly better than NAIST training.


We tackle the above issues by borrowing solutions from word representations, which have been intensely studied in the NLP community (Turian et al., 2010).

distributed representations of words have been used for many NLP problems, which represent a word by information from the words it frequently co-occurs with (Lin, 1997;Curran, 2005;Collobert et al., 2011;Bengio, 2009;Mikolov et al., 2013b).


We model the task of identifying the two types of supports as a binary classification problem.

we classify each sentence in a user message as containing either emotional or informational support 2 .


Table 1 contains a user message with sentences marked with the type of support in them.

given a sentence s, in a user message, we want to classify it into one of the two classes: emotional support or informational support.


To capture this difference, we use frequencies of subjective words and cancer-related keywords as features.

we design five features to code frequencies of weak subjective words (numWeak), strong subjective words (numStrong), cancer drugs (numDrug), sideeffects of cancer medications (numSide), and cancer procedures (numProc) respectively in a sentence.


Since, chi-square method gives feature ranking for the class variable and not for individual classes, we compute word rankings for individual classes using tf − idf scores of words.

for a term t and a class c, we compute the term frequency of t by counting its number of occurrences in the instances (sentences) belonging to c and multiply the term frequency with the inverse document frequency of t (calculated from the entire corpus) to get the tf − idf score of t for c. Using this method, we calculated tf − idf scores for all the words and ranked them according to their scores for the two classes.


The goal of ICA is to estimate both A and S. Once the mixing matrix A is estimated, the demixing matrix can be obtained by 1 W A   , and Eq.

(4) can be re-written as the observed mixture signals can be separated into independent components using the demixing matrix.


To identify multiple emotion labels contained in test examples, each emotion label presented in Table 1 was trained a binary classifier in the training phase.

for each method presented above, we built seven binary classifiers so that they can output multiple positive results to indicate that a test example contained multiple emotion labels.


By carefully investigating our Foursquare dataset, we found that approximately 75% users tend to cluster together to create tightly knit groups characterized by a relatively high density of friendship relations or ties, and the remaining 25% users loosely or seldom connect with each other through the friendship relations.

75% users form high density of relationship ties and the average clustering coefficient (ACC) is high (0.61).


Compared to the baseline methods (especially the SVM and ERGM methods), the performance improvement of our MLRG model mainly comes from 75% users with high density of friendship ties.

about 20% prediction errors (including social action and social tie prediction errors) of such users made by the SVM model can be corrected by our MLRG model.


It is particularly notable that our MGD algorithm takes much less time than other three algorithms for learning.

our proposed algorithm is over orders of magnitude faster than the LBP for running.


Experimental results also illustrate the feasibility and effectiveness of our latent factors for exploring the mutual influence.

the latent factors in our model significantly improve the F-measure by 8.27% (from 80.19 to 88.46) for social action prediction, and improve the F-measure by 6.0% (from 78.11 to 84.11) for social tie discovery, respectively.


Hence we need to evaluate a candidate noun phrase in terms of its frequency in the set of tweets for that entity, versus its frequency across all tweets in the same time window.

we define the uniqueness indicator of a phrase p in a time period s as: where I[p ∈ t] is an indicator that evaluates to 1 if the tweet t contains the phrase p, and 0 otherwise.


We use Bayesian information criteria (BIC) (Schwarz, 1978) to choose the optimal number of components for training.

we train models by varying K and pick the one with the largest BIC given by log p(X, y|θ m )− |θm| 2 log N where θ m is the model with m components having |θ m | parameters, N is the number of data points and log p(X, y|θ m ) for fixed θ m is given by eq.


In tweets extraction task, the model Ours (LTF+CCF) does not seem to be so dependent on the cross-media correlation features, but still there are five of them appearing important in the list.

the similarities between tweets and the leading news sentences such as SimiTopUnigram and LeadSenSimi are shown very helpful.


MKL consistently outperforms the single-kernel baseline for all p-norms.

instead of extensively experimenting with SSAD and different kernel functions and parameter selections, a single run with our MKL already leads to better performances in both metrics.


We then undertake a supervised classification approach to predict text aesthetics by constructing real-valued feature vectors from each text passage.

the features that we use for this classification task are word length, repetitions, polarity, part-of-speech, semantic distances; and topic generality and diversity.


With the availability of such highlighted text, which may be considered as text passages which most readers find pleasing to read, an interesting research problem is to attempt automatic prediction of highlighted pieces of text.

given a text passage, the objective is to It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair.


The problem of text categorization can effectively be solved by the application of discrete categorical features, such as character n-gram frequencies and word frequencies.

the presence of characteristic words from a particular domain is a good indicator of the class of a document, e.g.


This section investigates the usefulness of the MC algorithm for the text aesthetics classification.

we show that for this one class classification problem, the MC algorithm can selectively refine the set of unlabeled samples and retrain the model for better separability  between the positive and the unlabeled classes.


Since text coherence is a matter of degree rather than a binary classification, in both evaluation tasks we formulate the problem as a pairwise preference ranking problem.

given a set of texts with different degrees of coherence, we train a ranker which learns to prefer a more coherent text over a less coherent counterpart.


In this paper we explore how lexical chains can be employed to measure coherence in essays.

our goal is to investigate how attributes of lexical chains can encode discourse coherence quality, such as adherence to the essay topic, elaboration, usage of varied vocabulary, and sound organization of thoughts and ideas.


In this paper, we use this underlying principle of cohesion to detect the quality of coherence in a discourse.

we employ lexical chains to quantify and represent expectations for coherent discourse in test-taker essays.


Good writers usually initiate topics, ideas or claims and provide clear elaborations or reasons.

a sequence of many related words and phrases will be evoked to explain an idea or provide an account of the writer's reasoning.


We use essays from different essay-writing tasks, representing different genres, writing proficiency and populations.

our essays consist of the following six subsets: 1.


(Attali and Burstein, 2006;Burstein et al., 2013b)).

they are based on more than 30 kinds of errors in grammar, such as subject-verb agreement errors, in word usage, such as missing article errors, and in spelling.


A 10-fold cross-validation was run with an unscaled, gradient boosting regressor 2 tuned using quadratic weighted kappa 3 .

we used the standard Gradient Boosting Regressor in the scikit-learn toolkit 4 (Pedregosa et al., 2011).


Also, the addition of lexical chaining features produces performance improvement for most data types.

there is substantial improvement in performance for persuasive writing (PE-UG-NN and PE-G-N), expository subject writing (S-G-N) and writing involving critical argumentation (AC-G-N).


We performed detailed experiments which showed that lexical chaining features are useful for predicting discourse coherence quality.

when compared to other previously explored discourse-based features, we found that our lexical chaining features are best performers when used alone.


All of these search engines provide a way to quantify the commonness of a phrase and thus have the potential to become important tools for second language learners.


In this paper, we focus on temporal evidence classification.

assuming the passage retrieval component has retrieved the relevant passages as intermediate evidence of temporal relations, we must classify whether the time expression t in the passage belongs to one these classes: BEGINNING, END-ING, BEFORE, AFTER, and WITHIN.


We use Freebase as our reference knowledge base.

we use the temporal information found in Freebase to generate training examples.


Our error analysis on the temporal evidence classification task revealed several issues that inform our future work aimed at further improving the performance on the subtask of temporal evidence classification, and the overall temporal relation extraction task.

we intend to deal with the challenging aspect of semantic inference over relations found in the evidence passage.


We propose that the supervised classifiers perform essentially the same operation as the unsupervised measures, but are learning to determine the relevance of dimensions.

diff is learning weights on vector difference features.


Therefore, if the function words of a language are automatically detected and the co-occurrence statistics on the left and right of those words are appropriately analyzed, then it should be possible to tell the prepositional languages apart from the postpositional ones.

we measure counts and entropies of left, right and total (either side) co-occurrence distributions for each word.


To model the relationship between the grammar of definiteness and its communicative functions in a data-driven fashion, we work within the supervised framework of feature-rich discriminative classification, treating the functional categories from §2 as output labels y and various lexical, morphological, and syntactic characteristics of the language as features of the input x.

we learn two kinds of probabilistic models.


Another common confusion is between the highly frequent category Unique_Larger_Situation and the rarer category Unique_Hearer_New; the latter is supposed to occur only for the first occurrence of a proper name referring to a entity that is not already part of the knowledge of the larger community.

this distinction requires world knowledge about well-known entities, which could perhaps be mined from the Web or other sources.


In this paper, we concentrate on statistical approaches for modeling the SLU system.

conditional random fields (Lafferty et al., 2001) are used with a flat semantic frame to represent meaning and to model the SLU system.


4, reuse of margin M xc of common classification problem x c should minimize the number of remaining features included only in x.

x c should be as similar to x as possible (ideally, x c = x).


Finally, instead of building a parametric predictor for log linear weights, we experimented with a simple nearest-neighbor approach: where M j ranges over the linearly interpolated phrase tables, and λ i (M ) returns the stored optimal value for the i th log-linear weight, and: where s is the monolingual sample on which we want to calculate the BLEU-PT and s ranges over the source sides of our available parallel development sets.

a BLEU-PT of a model is calculated on the source sample to be translated and the log-linear weight is chosen which corresponds to BLEU-PT , where BLEU-PT is a training data point closest to BLEU-PT.


Our hypothesis is that enabling semi-supervised learning is effective in compensating for the undersegmentation caused by the lack of hierarchy.

semi-supervised learning can improve modeling of suffixation.


A dependency relation in Japanese is a modification relation in which a modifier bunsetsu depends on a modified bunsetsu.

the modifier bunsetsu and the modified bunsetsu work as modifier and modifyee, respectively.


Furthermore, the search space of O can be narrowed down to the word order patterns derived from the above narrowed dependency structures based on the conditions 2 and 3.

after dependency structures possible for an input sentence are narrowed down, we just have to find the word order patterns after reordering so that each of the dependency structures is maintained and satisfies the Japanese syntactic constraints even under the changed word order.


The reordering process is continued until the last reordered candidate is generated by moving M i,k after the last child.

the number of candidates generated by the reordering process is equal to the number of children of the last bunsetsu in M k+1,j .


In the experiment, as the test data, we used sentences generated by only changing the word order of newspaper article sentences in Kyoto Text Corpus (Kurohashi and Nagao, 1998), maintaining the dependency structure.

we artificially generated sentences which made sense but were not easy to read,  Figure 3: Example of test data generation in order to focus solely on problems caused by unsuitable word order.


Among existing approaches, graph-based semi-supervised learning algorithms have been shown to be an effective way to impose a query's influence on sentences (Zhou et al, 2003;Zhou et al, 2004;Wan et al, 2007).

a weighted network is constructed where each sentence is modeled as a node and relationships between sentences are modeled as directed or undirected edges.


And the edge weights are measured according to the similarities between nodes, which are computed based on the topic distribution from our two topic model extensions.

we introduce four edge weight matricesŴ N * K ,W K * N , U and P to describe the sentenceto-topic relations, the topic-to-sentence relations, the sentence-to-sentence relations and the topic-totopic relations respectively.


The score vectors can be achieved by minimizing the function in Equation (10).


Although there is a fair bit of research on generating naturallanguage descriptions of images (Feng and Lapata, 2013;Yang et al., 2011;Ordonez et al., 2011), there is significantly less work on describing videos (Barbu et al., 2012;Das et al., 2013;Rohrbach et al., 2013;Senina et al., 2014).

much of the research on videos utilizes artificially constructed videos with prescribed sets of objects and actions (Barbu et al., 2012;Yu and Siskind, 2013).


The observations in our model take the form of confidence scores from the visual detectors described in Sections 3.2 and 3.3.

the potential for each sentence component k ∈ {S, V, O, P }, φ k (t) = C k (t) is the detection confidence that the classifier for component k (C k ) gives to the word t. Language Potentials.


We compared using the vision system alone to our model, which augments that system with linguistic knowledge.

we consider the Highest Vision Confidence (HVC) model, which takes for each sentence component the word with the highest confidence from the state-of-the-art vision detectors described in Sections 3.2 and 3.3.


The performance differences between the vision system (HVC) and our integrated model (FGM) are modest but significant in important places.

the FGM makes improvements to SVO (  Table 2) component accuracies, suggesting that it can help clean up some noise from the vision systems even at the component level by considering related bigram probabilities.


In this paper, we explore the for-fun model (Cooper et al., 2010;Law et al., 2007;Von Ahn & Dabbish, 2004;Von Ahn et al., 2006), in which data is a by-product of some gameplay, often referred to as "Games with a Purpose" (Von Ahn & Dabbish, 2008), which have been shown to be very successful in many domains.

with respect to generation of paraphrases or phrase equivalents, (Chen & Dolan, 2011) present their paraphrase collection using video annotations, focusing primarily on viability of establishing Mechanical Turk for providing paraphrases in a productive way.


In this paper, we explored gaming as a methodology for generating paraphrase data that is useful for NLP or IR research and development of practical systems.

we outlined a game-with-apurpose -Doodlingthat is based on sketch-and-convey metaphor, where a sketch by a Drawer was used as a mechanism for abstracting a concept (the source phrase) which was then surfaced by different guessers in the game room, potentially producing paraphrases.


Among many entailment relations, our methodology has focused on the relations between nouns and verbs that represent similar concepts without sharing their roots.

verbs for nouns that do not share the same root are collected and then used to recognize textual entailment relations, such as the examples (3) and (4) above.


if the noun or any of the noun synonyms appears at the head position with any of rcmod, ccomp, parataxis, vmod, partmod, and infmod tag, then the word in the dependent position is captured.


Afterwards, particle verbs are captured by finding particles for each of the simplex verb.

if a dependency relation has a prep tag and has any one of the simplex verbs at the head position, then the word at the dependent position is regarded as a particle candidate.


Therefore, verbs in this group should be used to recognize as much relevant information as possible in spite of low precision.

this group is suitable for recall-oriented tasks.


Therefore, verbs in this group should be used to recognize as much accurate information as possible at the expense of low recall.

this group is suitable for precision-oriented tasks.


Consequently, verbs in this group should be used as a compromise between precision and recall.

this group is suitable for harmony-oriented tasks.


This is because DBpedia is easier to access from application viewpoint.

it already captured infobox property names from Wikipedia's article.


This implies that performance group model can be adopted for implementing tasks as a precision-oriented, recall-oriented, or even harmony-oriented as needed.

as the variability of natural language is hard to predict, this performance group model plays a very important role in guiding applications on whether to focus on getting high-quality information at the expense of large quantities of information, large quantities at the expense of high quality, or a compromise between these two extremes given limited available time and cost.


One of the distinguishing features of Wikipedia is that it is a hypertext, which greatly enhances its usefulness and usability.

an article is linked to its related articles in the same language via intra-language links as well as to its counterpart articles in different languages via inter-language links (ILLs), and readers can navigate within millions of articles.


A pair of corresponding anchors should be regarded as a single term and express the same sense in a discourse shared by and .

they should be linked to articles that are linked via an ILL.


To determine the most effective anchor priority score, the accuracy of anchors selected according to each score was evaluated, assuming the existing intra-language links in the original Japanese articles as gold standard.

anchor accuracy Acc is defined as the percentage of originally pointed destination articles for which correct anchors were selected, i.e., where is a set consisting of all transferred intra-language links and is the gold standard intra-language link set.


Although the positive word "很 好" (very good) is also used, it modifies the word "business" rather than the poster's condition.

the opinion targets of the two words are different, and this causes problems when automatically retrieving ironic messages.


Semantics: answer(exclude(river(all), traverse2(stateid( T N )))) As a step towards the above-mentioned goal, this work focuses on the development of an automated system that is capable of simultaneously parsing semantically equivalent natural language texts in different languages into their underlying semantics.

in this work, we first introduce a new variant of a semantic parsing model under an existing framework.


Each semantic representation consists of semantic units as its tree nodes, where each semantic unit is of the following form: Here m a is used to denote a complete semantic unit, which consists of its semantic type τ a , its function symbol p α , as well as a list of types for argument semantic units τ b * (here * means 0, 1, or 2; we assume there are at most two arguments for each semantic unit).

each semantic unit can be regarded as a function which takes in other semantic representations of specific types as arguments, and returns a new semantic representation of a particular type.


The hybrid tree model then focuses on the learning of these model parameters from the training data using maximum likelihood estimation.

the model tries to maximize: Since the correct hybrid tree associated with n-m pair is unknown, we marginalize over the hidden variable h. The model parameters will then be estimated using the Expectation-Maximization (EM) algorithm (Dempster et al., 1977).


In other words, the model tries to maximize: Since the correct hybrid tree associated with n-m pair is unknown, we marginalize over the hidden variable h. The model parameters will then be estimated using the Expectation-Maximization (EM) algorithm (Dempster et al., 1977).

an inside-outside style algorithm (Baker, 2005) is used where an additional layer of dynamic programming algorithms are used for efficient inference (Lu et al., 2008).


Note that the generation of natural language words involves the context Λ.

if the context is empty, the model is regarded as the unigram model.


Once the model parameters are learned, we will be able to use them to parse novel sentences.

for each novel input sentence, we first find the most probable hybrid tree that contains the sentence n, and then extract its internal nodes to form the semantic representation.


evaluation on the multilingual GEOQUERY dataset.

we first let our semantic parsers produce semantic representations from multilingual input sentences.


These voting-based approaches yielded better results over the first voting-based approach.

we compared this new voting-based approach against the previous best model reported in Lu et al.


Next we move to the multilingual setting where we would like to simultaneously process more than two languages.

we considered multilingual semantic parsing where there are two, three, four and five input languages.


• Lau (LDA) (Lau et al., 2012), who provide only the results for one of the three measures.

we compare our system to their results obtained by a model that was based on LDA and used the gold standard number of senses as the number of topics to be used.


In this section, we present some deductions drawn from the qualitative analysis of clusters generated by our methods which support our hypothesis.

we discus the nature of clusters generated by the spectral clustering algorithm using the second order distributional vectors for obtaining the similarity matrix based on Euclidean distance.


Usually, each individual database does not contain duplicate records, once we know r 1 is linked to r 1 , we can infer the rest candidate pairs should not be linked.

only if we are confident that no pair in the set {(r 1 , r 2 ), (r 1 , r 3 ), (r 2 , r 1 ), (r 3 , r 1 )} is not linked, r 1 is likely to be linked with r 1 .


Due to the asymmetry of rf, it only boosts the weights of terms that appear more frequently in the positive category.

rf discriminates against terms appearing more frequently in negative category.


One promising property of HDBN is that it can effectively use the distribution of large amount of unlabeled data, together with few label information in a unified framework.

hDBN can greatly reduce the dimension of reviews through RBM and abstract the information of reviews through the cooperate of RBM and CRBM.


We ask annotators to manually compress all the sentiment sentences.

the annotators removed some words from a sentiment sentence according to two criteria stated as follows: (1) removing the word should not change the essential content of the sentence, and (2) removing the word should not change the sentiment orientation of the sentence.


This illustrates that the idea of sentiment sentence compression is useful for T-P collocation extraction.

the proposed method can transform some over-natural sentences into normal ones, further influencing their final syntactic parsers.


a parser produces a projective structure, which in a second phase is transformed into a non-projective structure (Kahane et al., 1998;McDonald and Pereira, 2006;Nivre and Nilsson, 2005).

this may involve lifting, whereby one end point of a dependency link moves across a path of nodes.


a rule is of the form: where n ∈ N, The set of variables occurring in the lists x every variable from X m occurs exactly once in all these lists together.


Compared to PropBank and Semantic Frame stuctures, they have the advantage to be connected and complete, i.e., capture all argumentative, attributive and coordinative dependencies between the meaningful lexical items of a sentence, while PropBank and Semantic Frame structures are not always connected, may contain either individual lexical items or phrasal chunks as nodes, and discard attributive and coordinative relations (be they within the chunks or sentential).

they constitute incomplete structures that drop not only idiosyncratic, functional but also meaningful elements of a given sentence and often contain dependencies between chunks rather than individual tokens.


The edge labels of a SSyntS are grammatical functions 'subj ', 'dobj', 'det', 'modif', etc.

ssyntss are syntactic structures of the kind as encountered in the standard dependency treebanks; cf., e.g., dependency version of the Penn TreeBank (Johansson and Nugues, 2007) for English, Prague Dependency Treebank for Czech (Hajič et al., 2006), Ancora for spanish (Taulé et al., 2008), Copenhagen Dependency Treebank for Danish (Buch-Kromann, 2003), etc.


for a given SSyntS, the matrix I(i, j) contains in the cells (i, j), i, j = 1, .


Starting from the matrix I S of a given SSyntS, the task is therefore to obtain the matrix I D of the corresponding DSyntS, that is, to identify correspondences between i/j, (i, j) and groups of (i, j) of I S with i /j and (i , j ) of I D ; see (i)-(iii) above.

the task consists in identifying and removing all functional lexemes, and attach correctly the remaining nodes between them.


For our experiments, we use the AnCora-UPF SSyntS and DSyntS treebanks of Spanish (Mille et al., 2013) in CoNLL format, adjusted for our needs.

we removed from the 79-tag SSyntS treebank the semantically and information structure influenced relation tags to obtain an annotation granularity closer to the ones used for previous parsing experiments (55 relation tags, see (Mille et al., 2012)).


After several feature selection trials, we chose the following features for each node n: • lemma or stem of the label of n, • label of the relation between n and its head, After an optimization round of the parameters available in the SVM implementation, the hypernode identification achieved over the gold development set 99.78% precision and 99.02% recall (and thus 99.4% F1).

only very few hypernodes are not identified correctly.


For instance, the relation det in su←det−coche 'his/her car' and su←det−llamada 'his/her phone call' have different correspondences in DSyntS: su←AT T R−coche vs. su←I−llamada.

the DSyntS relation depends on the lexical properties of the governor.


Anticipation, creation, execution, collapse, deficiency of X can be seen as such phases of story concerning X, and PPTT classifies templates into 41 semantic classes each of which corresponds to a phase.

ppTT provides a way to describe the stories of various entities that constitute this world, and we believe that ppTT (partly) reflects how we understand the world and its entities.


Finally, we divided L1-classes into 41 L2-classes.

we first roughly grouped together semantically similar templates from the same L1-class and identified the common semantic properties among them.


The approach we propose seems quite competitive with other distributional models of semantics.

we match or outperform state-of-the-art methods on semantic compositionality tasks.


4) if the word of that node belongs to the corresponding POS class.

m3N-Expert-POS-class-1 is trained to optimize performance on words belonging to POS-class-1.


In this paper, we utilize the observed user's browsing behavior as a supervision signal for modeling interestingness.

we cast the prediction of ITs as a discriminative learning task.


We collect a large set of many millions of such user browsing events from session logs of a commercial web browser.

we collect from these logs each occurrence of a user click from one Wikipedia page to another during a one month period, from all users in all parts of the world.


It is important to note that editorlabeled category information is available in the Wikipedia domain but not in others.

we can use this information to verify that semantics indeed is influential for interestingness, but we should design our models to not rely on this.


Our goal in this task is to select anchors that maximize the cumulative degree of interest of the user, i.e.

: we consider the ideal selection to consist of the k most interesting anchors according to ( , ).We compare the interestingness ranking of our models against a gold standard function, ′ , computed from our test set.


A fundamental component which is common to all these models is the concept of a claim (or conclusion).

at the heart of every argument lies a single claim, which is the assertion the argument aims to prove.


In practice, we found this definition easy to convey to human labelers, and consequently feasible to capture by automatic detection methods.

we define the following two concepts: • Topic -a short phrase that frames the discussion.


The component receives an average of 1500 sentences per Topic and passes the top scoring 200 sentences to the next component.

we used Logistic Regression (LR) classifier due to its efficiency and its model interpretability, and focused our efforts on developing highly discriminative features for this classifier.


For computing the context-features, we use the topic as it appears in debatabase (see Section 3).

the most dominant features we identified included: MatchAtSubject: Cosine similarity between the Topic and the subjects of the candidate sentence -namely, all tokens that are marked as the subject of some sub-tree in the sentence ESG parse tree (McCord et al., 2012).


In addition to these Context features and Context-free features, we also developed a feature that represents a mix of these two types, that was proven essential to our performance, and relied on an extension of the Sequential Pattern Matching (SPM) algorithm (Srikant and Agrawal, 1996).

for this Se-quentialPatternMatch feature, each sentence token was encoded as a tuple describing several attributes for that token -e.g., the token's text, the token's POS tag, and various binary indicators, indicating if the token is a sentiment word, if it is mentioned in the given Topic, if it is included in an automatically learned lexicon of "claim words", and if it is identified by a NER utility (Finkel et al., 2005).


A variant of the SPM algorithm (Srikant and Agrawal, 1996) was then used to detect patterns that characterize CDCsentences, and these patterns were added to the features examined by the LR classifier.

each of these feature values was set to 1 if a candidate sentence had a match with the relevant pattern, and to 0 otherwise.


The problem with this approach, as we see it, is that it greatly complicates the annotation guidelines and the associated annotation work.

without a topic, it is less clear how to define what a claim is, and deciding when a sentence is related to the topic is bound to be highly subjective.


Boundaries Coarse Filter: This sub-component is based on a Maximum Likelihood probabilistic model that given a sentence, selects the 10 sub-sentences whose boundaries most probably correspond to a CDC.

given a sentence, for each of its sub-sentences 1 we consider the token preceding it; the token with which it starts; the token with which it ends; and the token following it, where a token here can be a word or a punctuation mark.


Importantly, though, the Context-free features examined by this sub-component relied on the division of the entire sentence, as implied by the examined boundaries.

the candidate boundaries induce a division of the containing sentence into three parts: prefix, candidate body, and suffix, where the prefix and/or suffix might be empty.


As shown in Table 4, this happens for 98% of the CDC-containing sentences (see Table 4).

if the examined sentence included a CDC, the Boundaries Coarse Filter almost always included this CDC as one of the top 10 sub-sentences it suggested for that sentence.


The goal of the analysis is to understand the types of errors the system makes as well as to obtain feedback on text spans that were not originally detected by the labelers (possible misses).

the labelers were instructed to choose one of the options in the following list: Accept -The candidate should be accepted as is.


Our primary motivation is to create a corpus for argumentative writing support and to achieve a better understanding of how arguments are represented in texts.

the contributions of this paper are the following: First, we introduce a novel annotation scheme for modeling arguments in texts.


Although, this approach is more time-consuming than a direct identification of argument components, we show in our annotation study (section 5) that it yields reliably annotated data.

the annotation guidelines consist of the following steps: 1.


In example 5, the Twitter user quotes Elbaradei stating that he may run for presidency if the people want him to.

the holder of the epistemic modality is actually Elbaradei not the Twitter user.


If we do not want to make any assumptions regarding possible priors for either gold standards or annotation biases (i.e., if we opt for the default assumption of uniform priors), then we can instead work with p(A j | g j = k).

we should choose k over Taking logarithms on both sides, we see that giving each annotator a weight of log (|K|−1) results in an optimal aggregator.


The second possible refinement is to iterate the process used to estimate acc i , i.e., to use Agr in place of SPR to compute better estimates agr i of acc i , and so forth.

we could use the EM algorithm (Dawid and Skene, 1979) to estimate acc i .


Recall that the gold standard for this dataset, arguably, overuses category 2 (see Footnote 11 and Section 3.4).

highquality annotators are (wrongly) judged to be underusing category 2.


By projecting the tensor to matrices, they showed that diverse concepts used in distributional semantics could be captured in a unified manner.

their tensors capture directly the duality of entity pairs and their linking expressions (i.e.


These previous works implicitly assume that the semantic space of entity pairs and that of relation ex-pressions are tightly coupled.

the space of entity pairs is defined in terms of their co-occurrences with linking expressions (or the weights in a tensor between them) and vice versa.


We denote a relation expression by r i and the whole set of relation expressions by D. We call a subset of relation expressions which manifest, directly or indirectly, a relation R, as the manifestation set of R. A relation, R, is characterized by the two sets, the extension set and the manifestation set.

the two sets are implicitly associated with each other via the relation R. This association between the two sets constitutes the foundation of the common semantic space to be constructed in this paper.


Some notes about sense frequency: by using only sense distribution to select a sense we obtain an algorithm that performs like the most frequent sense (MFS).

the algorithm always assigns the most probable meaning.


This approach is strongly related to our, although our approach directly computes the overlap in the geometric space that implements the distributional semantic model.

we build a vector representation for both the gloss and the context.


In this paper we describe an unsupervised WSD approach which selects the best sense according to the distributional similarity with respect to the context.

the similarity is computed representing both the gloss and the context as vectors in a geometric space generated by a distributional semantic model based on LSA.


As future work, we plan to extend our evaluation to other languages, and to investigate how to adapt our approach to a specific domain.

distributional models built upon a domain corpus, and sense frequencies extracted from the same corpus, could result in a domain adaptation of our algorithm.


The training data in the model contains 20K instances: 10K instances of target word, 10K auxiliary instances.

we used the JGibbLDA 1 framework for topic model estimation and inference, and examined the following LDA parameters: number of topics K, dirichlet hyperparameters for document-topic distribution α and topic-term distribution β.


We follow human development, acquiring coarse-grained categories and distinctions before detailed ones (Mandler, 2004).

we focus on the major class of concrete "things" (Langacker, 2008, Ch.


Nouns are interesting because they are the most basic lexical semantic categories.

children acquire nouns before any other category (Clark, 2009).


It then selects those flexible patterns in which both CWs are interchangeable.

it selects a pattern p if every word pair CW 1 , CW 2 that participates in p indicates with high probability that the word pair C 2 , C 1 also participates in p. For example, for the symmetric pattern "CW and CW", both "cats and dogs" and "dogs and cats" are semantically plausible expressions, and are therefore likely to appear in a large corpus.


Our experiments are designed to explore two main questions: (a) the value of symmetric patterns as semantic classification features, compared to state-of-the-art word clustering and embedding methods; and (b) the required complexity of an algorithm that can propagate information about semantic similarity.

we test the value of our simple I-k-NN algorithm compared to more sophisticated alternatives.


With respect to objective (a), the table clearly demonstrates that symmetric patterns lead to much better results compared to the alternatives.

for all four semantic categories, and across both evaluation measures, it is a model that utilizes symmetric pattern classification features that achieves the best results.


In this paper, because of the need for lexicon maintenance, we focus on relatively-new word-senses.

we consider the identification of word-senses that are not attested in a reference corpus, taken to represent standard usage, but that are attested in a focus corpus of newer texts.


(2012), to detect novel senses of a target word using this WSI method, we jointly topic model two corpora: a reference corpus -taken to represent standard usage -and a focus corpus of newer texts potentially containing novel senses.

we extract usages of a target word w from both corpora, and then topic model the pooled instances of w. Under this approach, the discovered topics are applicable to both corpora, so there is no need to reconcile two different sets of topics.


Recall that the goal of the optimisation is to find the ranking function f (G, p) that takes the smallest value for the best summary.

for the i-th training instance (G i , p i ), we aim at fulfilling the constraints for all p ∈ P( S , E )\p i .


The idea behind cutting planes is to instantiate only a minimal subset of the exponentially many constraints.

for the i-th training example, we decode the shortest pathp given our current model and consider two cases: (i) Forp = p i the prediction is erroneous andp is called the most strongly violated constraint as it realises the smallest function value and f (G i ,p) < f (G i , p) holds for all p =p.


With these notations, our proposed method can be formulated as the following optimization problem: where sent(g) and adj(g) indicate a set of sentences in g and a set of adjacent sentences in g, respectively.

our model tries to find the best sequence of sentences under the knapsack constraint so as to maximize the sum of weights and sentence coherence.


The other is a WordNet-based semantic similarity score between pairwise dependency relations from two sentences (Dasgupta et al., 2013).


In practice, bigrams are used as concepts.

let us use i to index all the concepts from the original corpus.


We can see that each degenerate version of our method performs worse than the complete method, which shows that all components of the objective function are useful.

entity coverage and section-specific relevance seem to be the more important components.


Since the instances belonging to the three "no relation" classes significantly outnumber those belonging to the remaining eight classes, we reduce data skew by downsampling  instances belonging to the three "no relation" classes.

2 we downsample the instances belonging to the three "no relation" classes (i.e., NTrP, NTeP, and NPP) by ensuring that (1) the ratio of the number of NTrP instances to the number of TREATMENT-PROBLEM instances is 0.06; (2) the ratio of the number of NTeP instances to the number of TEST-PROBLEM instances is 0.03; and (3) the ratio of the number of NPP instances to the number of PROBLEM-PROBLEM instances is 0.5.


While we want to use a parse tree directly as a feature for representing an instance, we do not want to use the entire parse tree as a feature.

while using the entire parse tree enables a richer representation of the syntactic context of the two concepts than using a partial parse tree, the increased complexity of the tree also makes it more difficult for the SVM learner to make generalizations.


To strike a better balance between having a rich representation of the context and improving the learner's ability to generalize, we extract a subtree from a parse tree and use it as the value of the structured feature of an instance.

given two concepts in an instance and the associated syntactic parse tree T , we retain as our subtree the portion of T that covers (1) all the nodes lying on the shortest path between the two entities, and (2) all the immediate children of these nodes that are not the leaves of T .


To see why dependency relations could be useful for MR classification, consider the sentences in Table 3: (1) (2) In sentences (1) and (2), the PROBLEM concepts His pain and The patient's pain occur as the subject of the verb controlled and the TREATMENT concepts oral medication and Motrin occur as objects of the prepositional with modifier of the same verb controlled.

intuitively, the verb controlled cues that the PROBLEM concept is being controlled, and together with the preposition with it cues that the TREATMENT concept is doing the controlling.


6 Otherwise, this system will leave inst unclassified.

this system is precision-oriented, classifying only those instances it can classify confidently.


In our second system, we represent each concept pair as a lexical pattern.

we employ Generation Method 3 as described in the Similarity features in Section 3.1 to generate a lexical pattern from a concept pair.


In contrast, our third system employs patterns that are hand-crafted based on manual observations made on the training set.

we ask a human to identify the contexts of two concepts that are strongly indicative of a relation class.


how can each system compute the probability that a given test instance belongs to a certain class?


Since our constraints are intra-sentential, we formulate one ILP program for each sentence s in each training summary.

each ILP program contains 11×N s variables, where N s is the number of test instances formed from the concept pairs in s. there is one binary indicator variable x i,j,r for each relation class r of each test instance inst formed from concept i and concept j, which will be set to 1 by the ILP solver if and only if it thinks inst should belong to class r. Our objective is to maximize the linear combination of these variables and their corresponding probabilities given by the ensemble (see (3) below) subject to two types of constraints, the integrity constraints and the consistency constraints.


7 Evaluation Following the 2010 i2b2/VA evaluation scheme, we assume that (1) gold concepts and their types are given, and (2) a medical relation classification system is evaluated on all but the "no relation" types.

a system will not be directly rewarded if it correctly identifies a "no relation" instance, but will be penalized if it misclassifies a "no relation" instance as one of the eight relation types.


Given the above results, a natural question is: is an ensemble approach ever needed to combine the knowledge sources exploited by different systems in order to obtain these improvements?

can we achieve similar performance by training a single classifier using a feature set containing all the features currently exploited by different members of the ensemble?


(2012) propose a first approach that exploits both natural and artificial data.

they select the most informative training points among natural and artificial examples, both coming from the PDTB or the RST DT.


A first set of methods involve adding new features.

we train a model on the artificial data, then run it on the natural examples.


It has been shown that this method provides an estimate of the error that is very close to that one could obtain on an independent evaluation set ( (Varma and Simon, 2006), (Scheffer, 1999)), as it prevents us from optimizing our hyper-parameters and performing evaluation on the same data.

there are two cross-validation loops: the inner loop is used for tuning the hyper-parameters (as described in section 6.2) and the outer loop estimates the generalization error.


With the basic technology supporting dialogue systems maturing, there has been more interest in recent years about dialogue systems that move beyond the traditional task-based or chatter bot frameworks.

there has been increasing interest in dialogue systems that engage in persuasion or negotiation (Georgila and Traum, 2011;Georgila, 2013;Paruchuri et al., 2009;Heeman, 2009;Mazzotta and de Rosis, 2006;Mazzotta et al., 2007;Nguyen et al., 2007;Guerini et al., 2003).


We focus on learning a policy that tries to satisfy both the user and system goals.

two elements in this work set it apart from previous works: • We introduce framing (Irwin et al., 2013), which is known to be important for persuasion and a key concept of this paper, as a system action.


This information is required to calculate reward described in the following Section 5.1.

gPF and CPD are used for calculating naturalness and persuasion success, which are part of the reward function.


Now that we have introduced the user model, we describe the system's dialogue management.

we describe the reward, system action, and belief state, which are required for reinforcement learning.


Learned policies (Framing, NoFraming) get a higher reward than Random.

both of the learned policies better achieve user satisfaction than Random.


Then, the evaluator starts the dialogue with the wizard who is obeying one of the policies (Figure 3).

dialogue between wizard and evaluator proceeds based on the following steps.


In addition, the score of NoFraming is lower than the score given by the user simulator.

persuasion success is drastically decreased.


In contrast, in our research, reward is defined on the basis of knowledge of human persuasive dialogue.

we calculate the reward and belief state using the predictive model of Hiraoka et al.


The negative correlation between image difficulty and median physician confidence was significant using Spearman's rank correlation (r s = −0.544, p < 0.005).

higher levels of case difficulty were associated with lower levels of physician confidence.


It is not surprising that the useful individual features from verbal and eye movement modalities were also useful when combined as multimodal features.

simultaneous silence and fixation were the most useful, which again might indicate contemplation and analytical cognitive processing.


The absence of (synchronic) semantic relatedness can have a number of reasons, including accidental instantiation of derivational patterns (corn -corner) and diachronic meaning drift (dog (animal) -dogged (determined)).

a substantial number of the lemma pairs in those lexicons are false positives regarding the level of semantic relatedness.


Secondly, and more seriously, the previous evaluation considered all instances of R and M as true positives.


The advantage of rank-based similarity is that it takes the density of regions in the semantic space into account.

a low cosine value does not necessarily indicate low semantic relatedness -provided that the two words are located in a "sparse" region.


In a similar way, , beginning with a seed set of noun/verb linguistic metaphor pairs, performed spectral clustering on a large set of nouns and verbs in order to predict metaphors which participate in the same conceptual metaphor mapping.

she modeled verbs according to their subcategorization frames parameterized by a model of their selectional preferences, while nouns were modeled according to the verbs with which they frequently co-occurred in a dependency relation.


2 Lin (1998), in developing a methodology for evaluating the quality of thesauri, defined a word vector space that moved beyond simple co-occurrence by integrating information about the relations between the word and its co-occurrents.

a word's vector was defined by the number of times that word occurred within a set of (word, relation, word) tuples.


Once the clusters have been identified, they can be used to define a mapping from the original LM ("cure") onto a pre-defined set of CM source domains (the MAPPING HYPOTHESIS).

individual concept candidates are mapped to CM domains by calculating the distance between the candidate and one or more vectors representing each domain in a high-dimensional distributional vector space.


The G-test is a measure of statistical significance for proportions, similar to the Chi-square test, which measures the degree to which a particular triple (w, r, w ) was found to occur more frequently than expected given all relations (w , r, w ).

if w occurs far more often with w than it does with other words, then it will receive a high G-test score for w. the G-test score is computed according to the following equation: where the index i ranges over the four cells of a 2x2 contingency table, O i is the observed count in cell i, and E i is the expected count in the same cell.


In this section, we describe our methodology for inducing conceptual domains for a linguistic metaphor by adapting techniques for unsupervised word-sense induction (Erk and Padó, 2008;Korkontzelos and Manandhar, 2010;Hope and Keller, 2013).

we induce conceptual domains in an unconstrained manner by extracting the grammatical co-occurrents of an LM source term (i.e., the 'concept candidates') and clustering them into semantically-related concept clusters.


First, we analyze the impact of our choice of vector space.

we compare the use of our DepVec space to link concept candidates with source domains against two off-the-shelf vector space models -the continuous skip-gram model [W2V] (Mikolov et al., 2013) 11 and latent semantic analysis [LSA] (Landauer and Dumais, 1997).


In examining Twitter data for such non-standard tokens, we found that 55.0% of them were types (1) to (3) in Table 1, 4.5% were type (4), 20.1% were types (5) to (6), 2.7% were type (7), and the rest did not fall under any of these types since they were the result of dialects, typos, and other factors.

a large majority of the non-standard tokens fell under types (1) to (7).


For example, if the transformation table contains (q, logp(q))= (" (yoo), (you)", -8.39), (" (o), (o)", -7.56), and the input text includes the character sequence " " (tyoo), we generate a new sequence " " (tyou) and " " (tyoo).

we add new nodes " " (you) and " " (o) in the position of " " (yoo) and " " (o), respectively (see Fig.


We show that a particularly good source of not-so-distant supervision is linked websites.

with this source of supervision we are able to improve over the state-of-the-art for Twitter POS tagging (89.76% accuracy, 8% error reduction) and NER (F1=79.4%, 10% error reduction).


The idea here is to exploit the fact that many word types in a dictionary are actually unambiguous, i.e., contain only a single tag.

93% of the word types in Wiktionary 2 are unambiguous.


They also suggest that some evaluation data sets share more vocabulary with our training data than others.

we would expect better performance on FOSTER-TEST than on RITTER-TEST and HOVY-TEST in POS tagging, as well as better performance on FININ-TEST than on the other two NER test sets.


We assume a latent variable R (pRoperties of entities in the world), and build a generative model (that is, model the joint P (I, R, U )).

going from P (I, R|U ) and making certain independence assumptions, we arrive at we assume that R is only conditional on I, and U is only conditional on R, and we can move P (I) and P (U ) out of the summation, as they do not depend on R. This is an update model in the usual sense that the posterior (P (I|U )) at one step becomes the prior (P (I)) at the next.


The tool was tested on a dataset of 232 sentences and the initial results were encouraging.

most of the cases of mismatch were linguistic issues and need further discussion.


The difference in performance is significant (t-test, p = 0.042 for the news subcorpus and p = 0.0103 for the Wikipedia subcorpus).

the choice of the training dataset significantly affects the results, which indicates that there really are domain differences in uncertainty detection.


(2005), we then use symmetrization heuristics to combine forward and backward alignments, which is common practice in the SMT community.

we apply the popular grow-diag-final-and heuristics as implemented in the Moses toolbox (Koehn et al., 2007).


Even though this is a reasonable setup for training phrase-based SMT models (as presented in the previous section), the chosen symmetrization heuristics (grow-diag-final-and) may not be well suited for accurate annotation projection.

it is known that these heuristics focus on recall and tend to add many additional links that may not be useful for our projection task and even lead to some confusion as depicted in the example in Figure 3.


The skeleton B is considered being filled by the synchronous tree T , and P (T |B) is decomposed into conditional probabilities of bilingual constituents α and contexts β conditioning on B ij,pq , a Boolean variable indicating whether the under-consideration bi-span <i, j><p, q> is a syn span or not.

α ij,pq denotes the bilingual constituent spanning from i to j on source side sentence, and spanning from p to q on target side sentence.


Since constituents exhibits richer appearances than contexts, the proposed Bayesian prior over α ij,pq |B ij,pq is more complicate than that over β ij,pq |B ij,pq .

one of the non-parametric Bayesian priors, the Pitman-Yor-Process (PYP) prior, is defined on α ij,pq |B ij,pq .


Accordingly, the grammar learning process is no longer a unilaterally supervised training by hierarchical knowledge, but an interactive process between the knowledge representation induction and the grammar training.

the grammar induction is supervised by the knowledge and the structure of the taxonomy is learned simultaneously.


Therefore, we go in the opposite direction, and propose an extended version of split-merge learning to handle the multiple branches in the taxonomy.

we split each state into all the subcategories in the lower layer in the taxonomy even if it has multiple branches, train, and then measure for every two sibling subcategories in the same layer the loss in likelihood incurred when merging them.


In our neural reordering model, "liu wan de" and "liu wan" have very similar vector space representations yet different orientations (i.e., M and D).

training examples labeled with M, S, D are prone to be mixed with each other in the vector space.


To solve the problem, we extend the RNNTSM model with factorizing tuples in input layer, as shown in Figure 2(a).

it consists of an input layer x, a hidden layer h (state layer), and an output layer y.


This paper aims to fill these gaps by applying various class-based LM techniques to the challenging problem of translating into a morphologically rich language.

we focus on English-Russian, a language pair for which a fair amount of both parallel data and monolingual data has been provided by the Workshop of Machine Translation (Bojar et al., 2013).


2012obtain the best perplexity reduction when excluding frequent words from the class mapping.

each word with more than θ occurrences in the training corpus is assigned to a singleton class with word emission probability equal to 1.


Besides, we observe that a variety of class-based LM variants have been studied in speech recognition but not in SMT.

goodman (2001) presents a generalization of the standard class-based form where the word emission is also conditioned on the class history rather than on the current class alone.


First of all, our results have shown that careful modeling of class-to-word emission probabilitiesoften omitted from the models used in SMT-is actually important for improving translation quality.

we have achieved best results when using a refined variant of the original class-based LM, called fullibm, which had never been tested for SMT but only for speech recognition (Goodman, 2001).


The probability that where w t,i and |D i | denote the tth token in D i and the number of tokens in D i , respectively, since we use the trigram language model.

then, the distance from the distance is determined based on the ratio of the probabilities that each language model generates the language data.


However, it is not so straightforward because the distribution of each proficiency level varies depending on the English groups.

some of the 10 non-native Englishes contains no or very few low-proficiency essays 4 .


As explained above, language use is based on social gender and age identity, and not on biological sex and chronological age.

treating gender and age as fixed biological variables in analyzing language use is too simplistic.


In this paper, we demonstrated the successful use of the crowd to study the relation between language use and social variables.

we took a closer look at inferring gender and age from language using data collected through an online game.


We then run integer linear programming over the collection, selecting one sentence for each set in a way that satisfies global constraints such as lexical density, the ratio of hard words, and text length.

we regard RT not as an operation that works sentence by sentence, but one that works with text as a whole.


The following linear program (LP) represents our adaptation of Dras's method.

formula 3 represents the objective function, with 4 through 7 expressing constraints: subject to: x i,j denotes a candidate for which we are to make a decision on whether to include it in the simplification of a given text d. we mean x i,j to represent the j-th best compression for the i-th sentence in d. Constraint 4 dictates that we have exactly one compression candidate for each sentence in d. w ij indicates the number of changes or deletions we performed on the original sentence to create x ij : -1 if x ij has one less term than the original sentence it is a compression of; 0 if there is no change.


As seen, our HYB system, and the individual components TS and RT all outperform QTSG with all three metrics.

tS is comparable to the SEW version when one looks at the median scores.


As mentioned earlier, 370 of these utterances contain a pointing gesture.


The semantics is as follows: under an optimistic estimate h(n) (= underestimating costs or overestimating merits) the total cost (the overall quality) for solving ACROSTIC GENERATION via a path along node n is always larger (smaller) than f (n).

g(n) accumulates the true cost (the achieved quality) for the partial acrostic via a concrete path s = n 0 , n 1 , .


8requires to enumerate all the n translations and retrieve for n times.


Recently linguistically-motivated models have been intensively investigated in MT.

source tree-based models (Liu et al., 2006;Huang et al., 2006;Eisner, 2003;Zhang et al., 2008;Liu et al., 2009a;Xie et al., 2011) have received growing interest due to their good abilities in modelling source language syntax for better lexicon selection and reordering.


We choose to lean our guidelines on the latter model, since it accounts also for different ways in which causal concepts are lexicalized.

wolff (2007) claims that causation covers three main types of causal concepts, i.e.


The mentioned binary features are introduced to exclude the corresponding token as a candidate token for a causal signal.

if a token is part of a named entity or an event, it is very unlikely that it will be part of a causal signal.


To predict types of each entity mention, we first use search engines to expand entity mentions.

each entity mention is used as a query sent to the search engine 3 .


A more severe one is that it is rather small: This means that statistical variation is strong (which will be illustrated in section 5.3), and that overfitting can easily happen.

a system trained on this data may not well perform on other data.


In analogy, we used the following procedure: For all words with a BNC corpus frequency of 50 or higher we computed the cooccurrence vectors.

each vector contains the number of co-occurrences of the stimulus word with all other co-occurring words.


By drawing an analogy between tokens and phrases in syntactic parsing with mentions and clusters in coreference resolution, we derive an algorithm for the latter.

we can initialise a queue of mentions and maintain a stack of clusters which incrementally grow as we read a document.


As each mention comes to head the queue, we generate a training instance in which the classifier decides whether it is more likely that the mention shift onto the stack as the first mention of a new discourse entity or reduce with the cluster of an already active one.

the reduce score initialise queue; initialise stack; while queue do active = queue.pop(); prediction = classify(active, stack); gold = correct classification(active, stack); if prediction != gold then update(prediction, gold); end cluster = apply pred(active, stack, gold); promote(cluster, stack); end initialise queue; initialise stacks; while queue do active = queue.pop(); forall stacks do prediction = classify(active, stack); cluster = apply pred(active, stack, prediction); promote(cluster, stack); end prune stacks(stacks); end is the highest of all potential merges.


6 System analysis Since we use simple, linear learning, it is possible to analyse feature weights to introspect system performance.

we would like to understand why our stack competition features, which are well principled, did not give a substantial performance gain.


This should be true under the assumptions that 1) people avoid generating a sentence that causes difficulty for them, and 2) center-embedding is a kind of such structure.

we focus on analyzing the oracle transitions of the system, i.e., parser actions to recover the gold dependency tree for a sentence.


wh-questions with auxiliary inversion, propositional complements, conjoined sentences) as complex patterns over parse trees, we propose that child language development can instead be measured automatically in a way that is fully data-driven and can be applied to many languages for which accurate dependency parsers are available, without relying on carefully constructed lists of grammatical structures or complex syntactic patterns in each language.

we examine two hypotheses: (1) counts of features extracted from syntactic parse trees using only simple templates are at least as expressive of changes in language development as the Index of Productive Syntax (Scarborough, 1990), an empirically validated metric based on an inventory of grammatical structures derived from the child language literature; and (2) these parse tree features can be used to model language development without the use of an inventory of specific structures, assuming only the knowledge that in typically developing children the level of language development is correlated with age.


Automatically extracting events from free texts is a higher-level Information Extraction (IE) task, which is still a challenge due to the complexity of natural language and the domain-specific nature, especially in Chinese for its specific characteristics.

most of previous studies have focused on English event extraction, while only a few concern Chinese.


Commonly, there are two head morphemes in a two-morpheme word of Coordination structure.

a two-morpheme word triggers an event if its two head morphemes are homogeneous (e.g., 攻(attack)击(attack), 死(die)亡(die)).


Since the mentions of the same word or similar words are often coreference ones and always refer to the same event type, we propose an event inference mechanism on coreference events to recover missing event mentions based on head morpheme as follows.

head morphemes are also identified following Li and Zhou (2012).


It's obvious that these inference mechanisms interact with others.

almost 20% event mentions can be inferred by both CE and RE for the transitivity of event inference on coreference and relevant events.


To avoid overfitting our statistical model, we do not count a word as appearing with itself.

once we have sub-corpus document frequences df T w p for each p, for each profile word p we define the element of our co-occurrence profile vector we normalize each count by the sum across all counts, such that the L1 norm of v is 1.


In this paper we address the question of the reliability of the predictions made by MT Quality Estimation (QE) systems.

we show that standard supervised QE systems, usually trained to minimize MAE, make serious mistakes at predicting the quality of the sentences in the tails of the quality range.


The quality of an (automatic) QE system cannot be perfect, but it should be at least controllable.

it should be possible to assess the reliability of the predictions made by a system, for instance by estimating the level of confidence of the predictions.


Hopefully, QE systems will progress towards this kind of behaviour, but currently the evaluation methods are not entirely satisfactory from this perspective.

after describing our experimental setting in §2, we will observe in §3 that the use of the Mean Absolute Error 1 (MAE) as a global evaluation measure hides huge discrepancies in the distribution of errors among the range of scores.


Furthermore, there is no visible impact of the quality of the MT output, although one would expect the correlation to be lower for low quality sentences: by definition, there are more differences between the MT output and the post-edited version for these sentences, so it should be easier for the system to detect the different level of quality between the two.

it is quite understandable that the system does not detect the difference for an MT output of relatively good quality, but the fact the post-edited version of the really bad translations are also rated as really bad is a major issue.


In this section we devise several experiments intended to explore different aspects of the problem in more detail.

we try to evaluate the impact of the possible causes described in §3: first we show in §5.1 that it affects most QE systems, especially those optimized to minimize MAE.


16 It is also worth noticing that the first system, which performs significantly better than the others, is the only one which was not optimized to minimize the MAE but to maximize the DeltaAvg score .

this system obtains a recall higher than the others in most cases (especially in the 5% and 10% tails), which is certainly due to the fact that it assigns more scores far from the mean (in other words, this system takes more risk).


Third, we show that when learning how to determine an ontological relationship between a pair of similar words by means of the word's distributional vectors, quite different vector operations are useful when identifying different ontological relationships.

using the difference between the vectors for pairs of words is appropriate for the entailment task, whereas adding the vectors works well for the co-hyponym task.


Whilst concatenating the vectors, and therefore retaining all of the information from both vectors including direction, generally performs well, we have also shown that different vector operations are useful in establishing different relationships.

the vector difference operation, which loses information about the original vectors, achieved performance indistinguishable from concatenation on the entailment task, where the classifier is required to distinguish hyponyms from other semantically related words including hypernyms.


To facilitate this, we firstly carry out a detailed static analysis of the differences that hold between event extraction tasks in the newswire and biomedical domains.

we consider the ACE 2005 event extraction task (Walker et al., 2006) for the newswire domain and the Genia Event Extraction task (GENIA) in BioNLP ST 2013 (Kim et al., 2013) for the biomedical domain.


To further investigate the differences in performance of the BioEE and Adapted BioEE systems on the two tasks, we evaluate the scores achieved for each task using the evaluation criteria originally designed for the other task.

we apply the ACE 2005 argument role classification criteria to the output of GENIA task, and we apply the complete event-based evaluation, originally used to evaluate the GENIA task, to the events extracted for the ACE 2005 task.


Due to the limited supervision provided by ESE (in most cases only 3-5 seeds are given), traditional ESE systems usually employ bootstrapping methods (Cucchiarelli and Velardi, 2001;Pasca, 2007;Riloff and Jones, 1999;Wang and Cohen, 2008).

the entity set is iteratively expanded through a pattern generation step and an instance extraction step.


 Figure 1: A demo of Bootstrapping (a) and Co-Bootstrapping (b) However, the traditional bootstrapping methods have two main drawbacks: 1) The expansion boundary problem.

using only positive seeds (i.e., some example entities from the category we want to expand), it is difficult to represent which entities we want to expand and which we don't want.


The second advantage of our method is that we can maintain and refine the expansion boundary during bootstrapping iterations, so that the semantic drift problem can be effectively resolved.

we propose an effective scoring algorithm to estimate the probability that an extracted instance belongs to the target category.


For demonstration, we will describe our method through the running example shown in Figure 1(b).

figure 2 shows the framework of our method.


This step generates and evaluates patterns using the statistics of the positive and the negative instances.

we propose three measures of pattern quality: the Generality (GE), the Precision of Extracted Instances (PE) and the Precision of Not Extracted Instances (PNE).


This step co-extracts the positive and the negative instances using highly confident patterns.

we propose an effective scoring algorithm to estimate the probability that an extracted instance belongs to the target category based on the statistics and the quality of the patterns which extract it.


For the second step, we propose three measures to evaluate the quality of a pattern, correspondingly the Generality (GE), the Precision of Extracted Instances (PE), and the Precision of Not Extracted Instances (PNE).

given a pattern, we observed that all instances can be categorized into four classes, according to whether they belong to the target category and whether they can be extracted by the pattern (shown in Table 1(a)).


A more general pattern will cover more entities than a more specific pattern.

the GE of a pattern is computed as: That is, the proportion of the instances which can be extracted by the pattern in the previous iteration.


The PE measures how likely an instance extracted by a pattern will be positive.

a pattern with higher PE will be more likely to extract positive instances than a lower PE pattern.


That is, a pattern with higher PE will be more likely to extract positive instances than a lower PE pattern.

the PE is computed as: the proportion of positive instances within all instances which can be extracted by the pattern in the previous iteration.


In this section, we describe how to compute P+ in the situation of multiple patterns.

we assume that an instance is extracted by different patterns independently.


(Brants and Franz, 2006) as our expansion corpus.

we use the open source package LIT-Indexer (Ceylan and Mihalcea, 2011) to support efficient wildcard querying for pattern generation and instance extraction.


We implement our method using two different settings: i) Hum_Co-Bootstrapping (Hum_CB): This is the proposed Co-Bootstrapping method in which the initial negative seeds are manually given.

we randomly select five positive seeds from the list of the category's instances while the initial negative seeds are manually provided.


Thus, we imagine that they can also be applied to other domains at a low cost.

other life-style domains, such as fashion, cosmetics or electronics show parallels, since comparable textual web data from which to extract domainspecific knowledge are available.


Additional experiments demonstrate that our approach is robust with respect to the lack of word boundaries and the presence of noise.

we use it to recover transliteration mappings between different scripts without parallel data, and to solve the Gold Bug riddle, a classic example of a substitution cipher.


The correspondence between the two script alphabets is not exactly one-to-one: Serbian Cyrillic uses 30 symbols, while Croatian Latin uses 27.

the Cyrillic characters ǉ, ǌ, and are represented in the Latin script as digraphs lj, nj, and dž.


We are interested in testing our approach on such ciphers, but since it is dependent on word language models, we need to first modify it to identify word boundaries.

the two components that require word boundaries are the scoring function (Section 3), and the search tree node expansion (Section 5).


Following this idea, we posit that information from the broader context in which a word has been uttered may simplify the learning problem faced by the child.

our hypothesis postulates that speech segmentation is easier when using vocabularies that are related to a specific activity (eating, Table 1: Most probable words in the 7 final topics   egg  book  ball  truck  name  color  block  apple  shape  cat  car  school  bear  battery  banana  square  hat  fire  time  crayon  minute  milk  circle  tree  piece  today  hair  phone  butter  triangle  fish  train  day  head  puzzle  ≈food ≈shapes ≈playing ≈toys ≈time ≈drawing "garbage" playing...), or place (kitchen, bedroom...).


The fact that no prefix performance is on par with the test condition means that contextual cues are not only important at test time, but particularly so while learning the vocabulary.

the model acquires its vocabularies making use of the additional context.


More generally, both models that learn specialized vocabularies do not get worse F-scores with increasing data (Figure 1).

they seem to fix a well-known problem of previous models like "colloc-syll" (our baseline), that "overlearn" by over-segmenting frequent morphemes as single words (Börschinger et al., 2012).


Then, the conversation-based context Λ C,l i of tweet t i (i.e., the target tweet) is the sequence of tweet iteratively built by applying r(•), until l tweets have been selected or r(•) = null.

l allows to limit the size of the input context.


emissions of x i by y i ) as well as interactions between neighboring labels y i along the chain (transitions).

φ is defined so that the complete labeling y = f (x) can be computed efficiently from F , using a Viterbi-like decoding algorithm, according to the linear discriminant function y * = arg max In the training phase, SVM hmm solves the following optimization problem given training examples (x 1 , y 1 ) .


As tweets are noisy texts, a pre-processing phase has been applied to improve the quality of linguistic features observable and reduce data sparseness.

a normalization step is applied to each post: fully capitalized words are converted in lowercase; reply marks are replaced with the pseudo-token USER, hyperlinks by LINK, hashtags by HASHTAG and emoticons by special tokens 5 .


Afterwards, an almost standard NLP chain is applied through the Chaos parser (Basili et al., 1998;Basili and Zanzotto, 2002).

each tweet, with its pseudo-tokens produced by the normalization step, is mapped into a sequence of POS tagged lemmas.


On the contrary, such poorer training evidence does not seem to afflict the contextual models in both corpora, as the markovian modeling seems to bring a straight benefit.

increasing amount of contextual information is usually beneficial to accuracy scores.


In this paper, we aim at developing a universal framework for transfer parsing that can exploit multityped source treebanks to improve parsing of a target treebank.

we will consider two kinds of source treebanks, that are multilingual universal treebanks and monolingual heterogeneous treebanks.


In this setting, we study the integration of multilingual universal treebanks.

we consider the DE, ES, FR, PT, IT and SV universal treebanks as target treebanks, and the EN treebank as the common source treebank.


Although not the primary focus of this work, we find that SMTL and MTL can be significantly improved in low resource setting through weighted sampling of tasks during training.

in the training procedure (Section 3.2.2), we sample from the source language (EN) which has a much richer treebank with larger probability of 0.9, while sample from the target language with probability of 0.1.


They first evaluate the importance f (s) of each sentence s and then select sentences to generate a summary based on both the importance scores and redundancy among sentences.

in this paper, we propose to model importance and redundancy simultaneously by directly evaluating the relative importance f (s|S) of a sentence s given a set of selected sentences S. we present a new framework to conduct regression with respect to the relative gain of s given S calculated by the ROUGE metric.


Existing sentence regression methods usually model sentence importance and sentence redundancy in two separate processes, namely sentence ranking and sentence selection.

in the sentence ranking process, they evaluate the importance f (s) of each sentence s with a ranking model (Osborne, 2002;Conroy et al., 2004;Galley, 2006; through either directly measuring the salience of sentences  or firstly ranking words (or bi-grams) and then combining these scores to rank sentences (Lin and Hovy, 2000;Yih et al., 2007;Gillick and Favre, 2009;Li et al., 2013).


Then, in the sentence selection process, they discard the redundant sentences that are similar to the already selected sentences.

in this paper, we propose a novel regression framework to directly model the relative importance f (s|S) of a sentence s given the sentences S. we evaluate the relative importance f (s|S) with a regression model where additional features involving the sentence relations are incorporated.


The first way is using the greedy strategy (Line 4 of Algorithm 1).

for each training episode of sentence s, we use the current model to generate the summary with greedy algorithm as a part of the S ′ .


The second way is randomly sampling a small set of s ′ with respect to its groundtruth ROUGE score (Line 6 of Algorithm 1).

for each training episode of sentence s, we sample a small set S ′ 2 according to the following rule: where rnd(0, 1) generates random number from a uniform distribution within the range Each sentence is selected with at least 5% probability and sentences with higher ROUGE scores have higher probabilities.


Although some steps can be run in parallel, the two modules interact, based on the assumption that the notion of causality is tightly connected with the temporal dimension and that information from one module can be used to improve or check the consistency of the other.

(i) TLINK labels for event-event (E-E) pairs, resulting from the rule-based sieve + temporal reasoner modules, are used as features for the CLINK classifier; and (ii) CLINK labels (i.e.


As for E-T pairs, we build a set of rules based on the temporal senses of some prepositions (Litkowski and Hargraves, 2006;Litkowski, 2014).

3 we assign a label whenever a temporal preposition establishes a dependency path between an event (E) and a timex (T), in which T acts as the temporal modifier of E. For example, if T is introduced by a temporal prepositions expressing a STARTTIME sense such as from or since, the relation is labelled as BEGUN BY.


We adopt the notion of causality proposed in the annotation guidelines of the Causal-TimeBank (Mirza et al., 2014;Mirza and Tonelli, 2014a), which accounts for CAUSE, ENABLE and PREVENT phenomena (Wolff, 2007;Wolff and Song, 2003) that are overtly expressed in text.

we aim at assigning a causal link to pairs of events when: (i) the causal relation is expressed by affect, link and causative verbs (CAUSE-, ENABLE-and PREVENT-type verbs), hereinafter simply addressed as causal verbs; or (ii) the causal relation is marked by a causal signal (see e.g.


Because of the increasing popularity of social media, much information has been shared on the internet, enabling social media users to understand various real world events.

social media-based infectious disease surveillance has attracted increasing attention.


Our approach takes the advantage of the increasing amount of parallel corpora in combination with wordnets in languages other than English for sense disambiguation, which will help us to improve automatic translations of English WordNet entries.

we assume that we have a multilingual parallel corpus consisting of sentences, x l i in a language l, grouped into parallel translations: we also assume that we have a collection of wordnets consisting of a set of senses, w l ij , grouped into synsets, for each language: we say that a context x l 0 i , in language l 0 (in our case this is always English), is disambiguated in n languages for a word w l 0 jk if: a context is disambiguated in n languages for a word, if for each of its translations we have a context in the parallel corpus that contains one of the known synset translations.


In this work we explore this idea in the context of neural encoder decoder architectures, albeit on a smaller scale and without MT as the end goal.

we consider the case of three languages or modalities X, Z and Y wherein we are interested in generating sequences in Y starting from information available in X.


As a small step in this direction, we consider a scaled down version of this generic n C 2 conversion problem.

we consider the case where we have three views X, Z, Y but parallel data is available only between XZ and ZY (instead of all 3 C 2 parallel datasets).


Instead, we suggest a neural network based model which simultaneously learns the following (i) a common representation for X and Z and (ii) decoding Y from this common representation.

instead of training two independent models using the datasets between XZ and ZY , the model jointly learns from the two datasets.


Further, in all their experiments they require parallel data between the two languages of interest.

they do not consider the case of generating sentences in Y given a sentence in X when no parallel data is available between X and Y .


Since we did not have such a corpus at our disposal we decided to follow (Rajendran et al., 2015) and use a pseudo parallel corpus between English-French.

we take the English captions from the MSCOCO data and translate them to French using the publicly available translation system provided by IBM (http://www.ibm.com/ smarterplanet/us/en/ibmwatson).


In this paper, we considered the problem of pivot based sequence generation.

we are interested in generating sequences in a target language starting from information in a source view.


We contend, however, that having a universal PoS inventory is only a first step towards making the source and target languages more alike.

these shared representations may hide fundamental differences in word order between source and target languages.


It is conceivable to use different tasks than historical spelling normalization as the auxiliary task in a multi-task learning setup.

we also experimented with grapheme-to-phoneme mapping as the auxiliary task, since it can be seen as a similar form of character-based sequence transduction.


In this paper, we empirically explore a neural network model to learn document-level representation for detecting deceptive opinion spam.

given a document, the model learns sentence representations with a convolutional neural network, which are combined using a gated recurrent neural network with attention mechanism to model discourse information and yield a document vector.


In this paper, we empirically investigate the effectiveness of learning dense document representations for opinion spam detection.

we propose a three-stage neural network system, as shown in Figure 1.